Effective compressive sensing for clustering in wireless sensor networks

Abstract

Wireless sensor Networks consists of a huge number of little powered nodes typically in the range of hundreds to thousands in number that are multifunctional and randomly deployed in a hostile environment. If a node detects an abnormal event, it will automatically send a hop by hop warning message to the sink node. There are various challenges and design issues in WSN like node deployment, routing, energy consumption, clustering, fault tolerance, coverage, connectivity and QoS i.e. Quality of Service. The author proposed a clustering approach that adopts a hybrid Compressive Sensing (CS) for sensor networks. The method compares the number of transmissions in the data aggregation techniques commonly used: Shortest path tree with hybrid compressive sensing, clustering without compressive sensing, optimal tree with hybrid compressive sensing, shortest path tree without compressive sensing, clustering with hybrid compressive sensing. Compressive sensing uses the largely similar data of large scale Wireless sensor networks and aims to minimize data transmissions without compromising the precision of the result obtained from data. The author finds that in comparison to these methods the proposed approach can reduce the data transmissions.